Astragaloside IV Alleviates Intestinal Barrier Dysfunction via the AKT-GSK3ß-ß-Catenin Pathway in Peritoneal Dialysis.

Background and aims: Long-term peritoneal dialysis (PD) causes intestinal dysfunction, including constipation, diarrhea, or enteric peritonitis. However, the etiology and pathogenesis of these complications are still unclear and there are no specific drugs available in the clinic. This study aims to determine whether Astragaloside IV (AS IV) has therapeutic value on PD-induced intestinal epithelial barrier dysfunction in vivo and in vitro. Methods: We established two different long-term PD treatment mice models by intraperitoneally injecting 4.25% dextrose-containing peritoneal dialysis fluid (PDF) in uremia mice and normal mice, which were served as controls. In addition, PDF was applied to T84 cells in vitro. The therapeutic effects of AS IV on PD-induced intestinal dysfunction were then examined by histopathological staining, transmission electron microscopy, western blotting, and reverse transcription polymerase chain reaction. The protein levels of protein kinase B (AKT), glycogen synthase kinase 3ß (GSK-3ß) and ß-catenin were examined after administration of AS IV. Results: In the present study, AS IV maintained the intestinal crypt, microvilli and desmosome structures in an orderly arrangement and improved intestinal epithelial permeability with the up-regulation of tight junction proteins in vivo. Furthermore, AS IV protected T84 cells from PD-induced damage by improving cell viability, promoting wound healing, and increasing the expression of tight junction proteins. Additionally, AS IV treatment significantly increased the levels of phosphorylation of AKT, inhibited the activity GSK-3ß, and ultimately resulted in the nuclear translocation and accumulation of ß-catenin. Conclusion: These findings provide novel insight into the AS IV-mediated protection of the intestinal epithelial barrier from damage via the AKT-GSK3ß-ß-catenin signal axis during peritoneal dialysis.

End-to-End Depression Recognition Based on a One-Dimensional Convolution Neural Network Model Using Two-Lead ECG Signal.

Purpose: Depression is a common mental illness worldwide and has become an important public health problem. The current clinical diagnosis of depression mainly relies on the doctor's experience and subjective diagnosis, which results in the low diagnostic efficiency and insufficient objectivity of diagnostic results. Therefore, establishing a physiological and psychological model for computer-aided diagnosis is an urgent task. In order to solve the above problems, this article uses a convolutional neural network (CNN) to identify depression based on electrocardiogram (ECG). Methods: Our method uses the raw ECG signal as the input of one-dimensional CNN, and uses the automatic feature processing layer of CNN to learn and distinguish signal features without additional feature extraction and feature selection steps. In order to obtain the optimal model, ECG segments of different durations (3 s, 4 s, 5 s and 6 s) and CNNs with different layers were used for comparison. In order to obtain modeling data, the resting ECG of 37 depression patients and 37 healthy controls were collected. In the proposed network, larger convolution kernels are used to better focus on overall changes. In addition, this article focuses on the inter-patient data classification standard, where the training and test sets come from different patient data. Results: Through comprehensive comparison, the 5 s ECG segment and 5-layer CNN are recommended in related applications. The proposed approach achieves high classification performance with accuracy of 93.96%, sensitivity of 89.43%, specificity of 98.49%, positive productivity of 98.34%. Conclusion: The experimental results indicate that the end-to-end deep learning approach can identify depression from ECG signals, and possess high diagnostic performance. It also shows that ECG is a potential biomarker in the diagnosis of depression.

Automated detection of clinical depression based on convolution neural network model.

As a common mental disorder, depression is placing an increasing burden on families and society. However, the current methods of depression detection have some limitations, and it is essential to find an objective and efficient method. With the development of automation and artificial intelligence, computer-aided diagnosis has attracted more and more attention. Therefore, exploring the use of deep learning (DL) to detect depression has valuable potential. In this paper, convolutional neural network (CNN) is applied to build a diagnostic model for depression based on electroencephalogram (EEG). EEG recordings are analyzed by three different CNN structures, namely EEGNet, DeepConvNet and ShallowConvNet, to dichotomize depression patients and healthy controls. EEG data were collected in the resting state from three electrodes (Fp1, Fz, Fp2) among 80 subjects (40 depressive patients and 40 normal subjects). After the preprocessing step, the DL structures are employed to classify the data, and their recognition performance is evaluated by comparing the classification results. The classification performance shows that depression was effectively detected using EEGNet with 93.74% accuracy, 94.85% sensitivity and 92.61% specificity. In the process of optimizing the parameters of EEGNet structure, the highest accuracy can reach 94.27%. Compared with traditional diagnostic methods, EEGNet is highly worthy for the future depression detection and valuable in terms of accuracy and speed.

Discrimination of vestibular function based on inertial sensors.

BACKGROUND AND OBJECTIVE: Vestibular dysfunction, as a common disease or symptom, can cause abnormalities in gait and balance. Since the existing detection methods are static detection and cannot obtain the dynamic vestibular information of patients, this paper proposes a simple method for detecting vestibular dysfunction based on gait signals of subjects. METHODS: In our study, the walking patterns of dynamic gait index (DGI) and inertial sensor were adopted for the data acquisition. Time-domain, frequency-domain and non-linear features were extracted from inertial sensor signals. Then the Relief algorithm was used for feature selection. Two classifiers, Support Vector Machine (SVM) and Random Forest (RF), were used to classify the patients with vestibular dysfunction and the healthy controls. RESULTS: The highest accuracy of 84.79% was achieved based on magnetometer features and SVM classifier. To further improve classification results, features of three sensor signals were combined and applied to two classifiers. Combined features and RF classifier achieved a classification accuracy of 86.5%. CONCLUSION: The detection of vestibular dysfunction based on inertial sensors might be simple, accurate and easy to implement in clinical examination, which provides a new method for the clinical diagnosis of vestibular function.

Berberine inhibits intestinal epithelial barrier dysfunction in colon caused by peritoneal dialysis fluid by improving cell migration.

ETHNOPHARMACOLOGICAL RELEVANCE: Berberine is generally extracted from Rhizoma Coptidis (Coptis chinensis Franch), a traditional Chinese medicine, which can be used in the treatment of intestinal diseases, respiratory infections and cardiovascular diseases. Berberine is especially effective for the treatment of gastrointestinal disorders such as diarrhea because of the effect of heat-clearing and detoxifying in traditional Chinese medicine theory. AIM OF THE STUDY: This study aimed to examine the protective effect of berberine (BBR) on the damaged colonic epithelial barrier caused by peritoneal dialysis fluid (PDF). METHODS: The damage to intestinal epithelial barrier was examined by intraperitoneally injecting 4.25% dextrose-containing PDF in mice and establishing a long-term PD model in rats with renal failure. Then, the therapeutic potential of berberine on PD-related colonic injuries was examined. T84 colonic epithelial cells were used to test the effect of PDF and berberine in vitro. The damaging effect of PDF and the protective effect of berberine were evaluated by histology staining, histofluorescence and transmission electron microscopy. The migration of colonic epithelial cell and actin-related protein 2 (Arp2) were tested by wound healing assay and Western blot to determine the possible mechanism in vitro. RESULTS: PD administration induced intestinal epithelial barrier dysfunction in the colon, and berberine alleviated the injury by increasing the tight junction and adhesion junction protein, both in vivo and in vitro. Berberine could also improve the morphology of microvillus. In the wound healing assay, berberine exhibited the ability to promote cell migration, indicating that berberine could probably recover the function of intestinal epithelial cells when the intestinal epithelial barrier was damaged by the PDF. CONCLUSIONS: The present study demonstrates that berberine can ameliorate intestinal epithelial barrier dysfunction in the colon caused by long-term PDF through improving cell migration.

Depression evaluation based on prefrontal EEG signals in resting state using fuzzy measure entropy.

OBJECTIVE: Depression is a mental disorder that causes emotional changes and even suicide. However, there is still a lack of objective physiological data to support current clinical depression diagnosis. Accurate computer-aided diagnosis systems are becoming more and more crucial and urgent for future depression diagnosis. The purpose of this study is to analyze the electroencephalogram (EEG) regularity of depression using fuzzy measure entropy (FMEn), and thus to explore its role in the computer-aided diagnosis of depression. APPROACH: Three-channel EEG signals among 35 subjects (divided into two groups according to the severity of the disease) were recorded in this study. First, the frontal delta, theta, alpha and beta frequency bands were extracted after preprocessing, and the sample entropy (SEn) and the FMEn were calculated. Then, the difference between the two groups and the correlation between the entropy values and the Hamilton Depression Rating Scale scores were analyzed using statistical analysis. Finally, the results of FMEn were compared with those of SEn. MAIN RESULTS: A better statistically significant difference between the two groups using FMEn was revealed, with p < 0.01 in the theta and alpha bands. In terms of SEn, only SEn_Fp2 in the delta band, SEn_Fp2 in the theta band and SEn_Fp1 in the alpha band performed better, showing significant differences with p = 0.0006, p = 0.002 and p = 0.0114. SIGNIFICANCE: These findings suggest that frontal EEG signal complexity analysis with depression using FMEn might be more sensitive than that using SEn. FMEn could be considered as a promising biomarker for future clinical depression detection.

Frontal Alpha Complexity of Different Severity Depression Patients.

Depression is a leading cause of disability worldwide, and objective biomarkers are required for future computer-aided diagnosis. This study aims to assess the variation of frontal alpha complexity among different severity depression patients and healthy subjects, therefore to explore the depressed neuronal activity and to suggest valid biomarkers. 69 depression patients (divided into three groups according to the disease severity) and 14 healthy subjects were employed to collect 3-channel resting Electroencephalogram signals. Sample entropy and Lempel-Ziv complexity methods were employed to evaluate the Electroencephalogram complexity among different severity depression groups and healthy group. Kruskal-Wallis rank test and group t-test were performed to test the difference significance among four groups and between each two groups separately. All indexes values show that depression patients have significantly increased complexity compared to healthy subjects, and furthermore, the complexity keeps increasing as the depression deepens. Sample entropy measures exhibit superiority in distinguishing mild depression from healthy group with significant difference even between nondepressive state group and healthy group. The results confirm the altered neuronal activity influenced by depression severity and suggest sample entropy and Lempel-Ziv complexity as promising biomarkers in future depression evaluation and diagnosis.

Cardiorespiratory Coupling Analysis Based on Entropy and Cross-Entropy in Distinguishing Different Depression Stages.

AIMS: This study used entropy- and cross entropy-based methods to explore the cardiorespiratory coupling of depressive patients, and thus to assess the values of those entropy methods for identifying depression patients with different disease severities. METHODS: Electrocardiogram (ECG) and respiration signals from 69 depression patients were recorded simultaneously for 5 min. Patients were classified into three groups according to the Hamilton Depression Rating Scale (HDRS) scores: group Non-De (HDRS 0-7), Mid-De (HDRS 8-17), and Con-De (HDRS >17). Sample entropy (SEn), fuzzy measure entropy (FMEn) and high-frequency power (HF) were computed on the original RR interval time series and breath-to-breath interval time series. Cross sample entropy (CSEn) and cross fuzzy measure entropy (CFMEn) were computed on interval time series resampled at 2 Hz and 4 Hz, respectively. The difference among three patient groups and correlation between entropy values and HDRS scores were analyzed by statistical analysis. Surrogate data were also employed to confirm the validation of entropy measures in this study. RESULTS: A consistent increasing trend has been found among most entropy measures from Non-De, to Mid-De, and to Con-De groups, and a significant (p < 0.05) difference in FMEn of RR intervals exists between Non-De and Mid-De or Con-De groups. Significant differences have been also found in all cross entropies, between Non-De and Con-De groups and between Mid-De and Con-De groups. Furthermore, significant correlations also exist between HDRS scores and FMEn of RR intervals (R = 0.24, p < 0.05), CSEn at 4 Hz (R = 0.26, p < 0.05) or 2 Hz (R = 0.28, p < 0.05) resampling, and CFMEn at 4 Hz (R = 0.31, p < 0.01) or 2 Hz (R = 0.30, p < 0.05) resampling. A significant difference of cardiorespiratory coupling parameters between different depression stages and significant correlations between entropy measures and depression severity both indicate central autonomic dysregulation in depression patients and reflect varying degrees of vagal modulation reduction among different depression levels. Analysis based on surrogate data confirms that the non-linear properties of the physiological signals played a major role in depression recognition. CONCLUSION: The current study demonstrates the potential of cardiorespiratory coupling in the auxiliary diagnosis of depression based on the entropy method.

Mechanism of Regulation of Big-Conductance Ca2+-Activated K+ Channels by mTOR Complex 2 in Podocytes.

Podocytes, dynamic polarized cells wrapped around glomerular capillaries, are an essential component of the glomerular filtration barrier. BK channels consist of one of the slit diaphragm (SD) proteins in podocytes, interact with the actin cytoskeleton, and play vital roles in glomerular filtration. Mechanistic target of rapamycin (mTOR) complexes regulate expression of SD proteins, as well as cytoskeleton structure, in podocytes. However, whether mTOR complexes regulate podocyte BK channels is still unclear. Here, we investigated the mechanism of mTOR complex regulation of BK channels via real-time PCR, western blot, immunofluorescence, and patch clamping. Inhibiting mTORC1 with rapamycin or downregulating Raptor had no significant effect on BK channel mRNA and protein levels and bioactivity. However, the dual inhibitor of mTORC1 and mTORC2 AZD8055 and short hairpin RNA targeting Rictor downregulated BK channel mRNA and protein levels and bioactivity. In addition, MK2206, GF109203X, and GSK650394, which are inhibitors of Akt, PKCα, and SGK1, respectively, were employed to test the downstream signaling pathway of mTORC2. MK2206 and GF109203X had no effect on BK channel protein levels. MK2206 caused an obvious decrease in the current density of the BK channels. Moreover, GSK650394 downregulated the BK channel protein and mRNA levels. These results indicate mTORC2 not only regulates the distribution of BK channels through Akt, but also modulates BK channel protein expression via SGK1 in podocytes.

Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series.

This study's aim was to apply permutation entropy (PE) and permutation min-entropy (PME) over an RR interval time series to quantify the changes in cardiac activity among multiple emotional states. Electrocardiogram (ECG) signals were recorded under six emotional states (neutral, happiness, sadness, anger, fear, and disgust) in 60 healthy subjects at a rate of 1000 Hz. For each emotional state, ECGs were recorded for 5 min and the RR interval time series was extracted from these ECGs. The obtained results confirm that PE and PME increase significantly during the emotional states of happiness, sadness, anger, and disgust. Both symbolic quantifiers also increase but not in a significant way for the emotional state of fear. Moreover, it is found that PME is more sensitive than PE for discriminating non-neutral from neutral emotional states.

Gene essentiality prediction based on fractal features and machine learning.

Essential genes are required for the viability of an organism. Accurate and rapid identification of new essential genes is of substantial theoretical interest to synthetic biology and has practical applications in biomedicine. Fractals provide facilitated access to genetic structure analysis on a different scale. In this study, machine learning-based methods using solely fractal features are presented and the problem of predicting essential genes in bacterial genomes is evaluated. Six fractal features were investigated to learn the parameters of five supervised classification methods for the binary classification task. The optimal parameters of these classifiers are determined via grid-based searching technique. All the currently available identified genes from the database of essential genes were utilized to build the classifiers. The fractal features were proven to be more robust and powerful in the prediction performance. In a statistical sense, the ELM method shows superiority in predicting the essential genes. Non-parameter tests of the average AUC and ACC showed that the fractal feature is much better than other five compared features sets. Our approach is promising and convenient to identify new bacterial essential genes.

Quantification of vascular function changes under different emotion states: A pilot study.

Recent studies have indicated that physiological parameters change with different emotion states. This study aimed to quantify the changes of vascular function at different emotion and sub-emotion states. Twenty young subjects were studied with their finger photoplethysmographic (PPG) pulses recorded at three distinct emotion states: natural (1 minute), happiness and sadness (10 minutes for each). Within the period of happiness and sadness emotion states, two sub-emotion states (calmness and outburst) were identified with the synchronously recorded videos. Reflection index (RI) and stiffness index (SI), two widely used indices of vascular function, were derived from the PPG pulses to quantify their differences between three emotion states, as well as between two sub-emotion states. The results showed that, when compared with the natural emotion, RI and SI decreased in both happiness and sadness emotions. The decreases in RI were significant for both happiness and sadness emotions (both P< 0.01), but the decreases in SI was only significant for sadness emotion (P< 0.01). Moreover, for comparing happiness and sadness emotions, there was significant difference in RI (P< 0.01), but not in SI (P= 0.9). In addition, significant larger RI values were observed with the outburst sub-emotion in comparison with the calmness one for both happiness and sadness emotions (both P< 0.01) whereas significant larger SI values were observed with the outburst sub-emotion only in sadness emotion (P< 0.05). Moreover, gender factor hardly influence the RI and SI results for all three emotion measurements. This pilot study confirmed that vascular function changes with diffenrt emotion states could be quantified by the simple PPG measurement.

Reprint of "GPR30 mediates estrogen rapid signaling and neuroprotection".

G-protein-coupled estrogen receptor-30 (GPR30), also known as G-protein estrogen receptor-1 (GPER1), is a putative extranuclear estrogen receptor whose precise functions in the brain are poorly understood. Studies using exogenous administration of the GPR30 agonist, G1 suggests that GPR30 may have a neuroprotective role in cerebral ischemia. However, the physiological role of GPR30 in mediating estrogen (E2)-induced neuroprotection in cerebral ischemia remains unclear. Also unclear is whether GPR30 has a role in mediating rapid signaling by E2 after cerebral ischemia, which is thought to underlie its neuroprotective actions. To address these deficits in our knowledge, the current study examined the effect of antisense oligonucleotide (AS) knockdown of GPR30 in the hippocampal CA1 region upon E2-BSA-induced neuroprotection and rapid kinase signaling in a rat model of global cerebral ischemia (GCI). Immunohistochemistry demonstrated that GPR30 is strongly expressed in the hippocampal CA1 region and dentate gyrus, with less expression in the CA3 region. E2-BSA exerted robust neuroprotection of hippocampal CA1 neurons against GCI, an effect abrogated by AS knockdown of GPR30. Missense control oligonucleotides had no effect upon E2-BSA-induced neuroprotection, indicating specificity of the effect. The GPR30 agonist, G1 also exerted significant neuroprotection against GCI. E2-BSA and G1 also rapidly enhanced activation of the prosurvival kinases, Akt and ERK, while decreasing proapototic JNK activation. Importantly, AS knockdown of GPR30 markedly attenuated these rapid kinase signaling effects of E2-BSA. As a whole, the studies provide evidence of an important role of GPR30 in mediating the rapid signaling and neuroprotective actions of E2 in the hippocampus.

GPR30 mediates estrogen rapid signaling and neuroprotection.

G-protein-coupled estrogen receptor-30 (GPR30), also known as G-protein estrogen receptor-1 (GPER1), is a putative extranuclear estrogen receptor whose precise functions in the brain are poorly understood. Studies using exogenous administration of the GPR30 agonist, G1 suggests that GPR30 may have a neuroprotective role in cerebral ischemia. However, the physiological role of GPR30 in mediating estrogen (E2)-induced neuroprotection in cerebral ischemia remains unclear. Also unclear is whether GPR30 has a role in mediating rapid signaling by E2 after cerebral ischemia, which is thought to underlie its neuroprotective actions. To address these deficits in our knowledge, the current study examined the effect of antisense oligonucleotide (AS) knockdown of GPR30 in the hippocampal CA1 region upon E2-BSA-induced neuroprotection and rapid kinase signaling in a rat model of global cerebral ischemia (GCI). Immunohistochemistry demonstrated that GPR30 is strongly expressed in the hippocampal CA1 region and dentate gyrus, with less expression in the CA3 region. E2-BSA exerted robust neuroprotection of hippocampal CA1 neurons against GCI, an effect abrogated by AS knockdown of GPR30. Missense control oligonucleotides had no effect upon E2-BSA-induced neuroprotection, indicating specificity of the effect. The GPR30 agonist, G1 also exerted significant neuroprotection against GCI. E2-BSA and G1 also rapidly enhanced activation of the prosurvival kinases, Akt and ERK, while decreasing proapototic JNK activation. Importantly, AS knockdown of GPR30 markedly attenuated these rapid kinase signaling effects of E2-BSA. As a whole, the studies provide evidence of an important role of GPR30 in mediating the rapid signaling and neuroprotective actions of E2 in the hippocampus.

[Fuzzy comprehensive evaluation method of metabolic syndrome based on PCA].

In modern society, metabolic syndrome (MS) occurs in a state in which many diseases gather in the human body and can directly lead to cardiovascular diseases and even to death. MS generates slowly, and the early intervention, the basis of which is the evaluation of MS, can effectively alleviate the diseases. This paper presents an MS fuzzy comprehensive evaluation method based on principal component analysis (PCA). The method firstly deals with the historical data that impact the indexes of MS to obtain the weight of each index by using PCA. Then it evaluates each index to get the human health score by using fuzzy comprehensive evaluation method, and therefore it provides objective basis for the early effective intervention. Finally we verified the feasibility and effectiveness of the method using demonstration and simulation.

[Biometric identification method for ECG based on the piecewise linear representation (PLR) and dynamic time warping (DTW)].

To treat the problem of identification performance and the complexity of the algorithm, we proposed a piecewise linear representation and dynamic time warping (PLR-DTW) method for ECG biometric identification. Firstly we detected R peaks to get the heartbeats after denoising preprocessing. Then we used the PLR method to keep important information of an ECG signal segment while reducing the data dimension at the same time. The improved DTW method was used for similarity measurements between the test data and the templates. The performance evaluation was carried out on the two ECG databases: PTB and MIT-BIH. The analystic results showed that compared to the discrete wavelet transform method, the proposed PLR-DTW method achieved a higher accuracy rate which is nearly 8% of rising, and saved about 30% operation time, and this demonstrated that the proposed method could provide a better performance.

[Acute encephalopathy due to late-onset maple syrup urine disease in a school boy].

Maple syrup urine disease is a common amino acids metabolic disease. In most patients, onset occurs in the neonatal period and infancy. In this study, the case of a school boy with acute encephalopathy due to late-onset maple syrup urine disease is summarized. The boy (8.5 years) was admitted because of acute encephalopathy after suffering from infection for two days at the age of eight and a half years. Metabolic acidosis, hyperuricemia and decreased protein level in cerebrospinal fluid were found by general laboratory tests. Magnetic resonance imaging of the brain revealed signal intensity abnormalities in the bilateral cerebellum dentate nucleus, brainstem, thalamus, putamen, caudate nucleus and cortex of the cerebral hemispheres. On T1WI and T2WI scanning, hyperintensive signal was found. Blood leucine and valine were significantly elevated. Urinary 2-hydroxy isovaleric acid, 3-hydroxybutyric acid, 2-keto isovaleric acid, and 2-keto acid also increased. Both the blood amino acid and urine organic acid profiles led to the diagnosis of maple syrup urine disease. In the acute period, the patient was treated with a large dose of vitamin B1, glucose, L-carnitine and a protein-restrict diet. The patient's condition improved significantly after five days of treatment, and he recovered completely two days later. Afterwards, treatment with vitamin B1, L-carnitine and a protein-restrict diet (1 g/kg/day) was continued. One and a half months later, blood amino acids and urine organic acids returned to normal. Magnetic resonance imaging of the brain also indicated a great improvement. It was concluded that inborn metabolic disease should be considered in the patients with an onset similar to acute encephalopathy. Early diagnosis and proper treatment can prevent brain damage and improve prognosis.

The PLR-DTW method for ECG based biometric identification.

There has been a surge of research on electrocardiogram (ECG) signal based biometric for person identification. Though most of the existing studies claimed that ECG signal is unique to an individual and can be a viable biometric, one of the main difficulties for real-world applications of ECG biometric is the accuracy performance. To address this problem, this study proposes a PLR-DTW method for ECG biometric, where the Piecewise Linear Representation (PLR) is used to keep important information of an ECG signal segment while reduce the data dimension at the same time if necessary, and the Dynamic Time Warping (DTW) is used for similarity measures between two signal segments. The performance evaluation was carried out on three ECG databases, and the existing method using wavelet coefficients, which was proved to have good accuracy performance, was selected for comparison. The analysis results show that the PLR-DTW method achieves an accuracy rate of 100% for identification, while the one using wavelet coefficients achieved only around 93%.

Role of Rac1 GTPase in NADPH oxidase activation and cognitive impairment following cerebral ischemia in the rat.

BACKGROUND: Recent work by our laboratory and others has implicated NADPH oxidase as having an important role in reactive oxygen species (ROS) generation and neuronal damage following cerebral ischemia, although the mechanisms controlling NADPH oxidase in the brain remain poorly understood. The purpose of the current study was to examine the regulatory and functional role of the Rho GTPase, Rac1 in NADPH oxidase activation, ROS generation and neuronal cell death/cognitive dysfunction following global cerebral ischemia in the male rat. METHODOLOGY/PRINCIPAL FINDINGS: Our studies revealed that NADPH oxidase activity and superoxide (O(2)(-)) production in the hippocampal CA1 region increased rapidly after cerebral ischemia to reach a peak at 3 h post-reperfusion, followed by a fall in levels by 24 h post-reperfusion. Administration of a Rac GTPase inhibitor (NSC23766) 15 min before cerebral ischemia significantly attenuated NADPH oxidase activation and O(2)(-) production at 3 h after stroke as compared to vehicle-treated controls. NSC23766 also attenuated "in situ" O(2)(-) production in the hippocampus after ischemia/reperfusion, as determined by fluorescent oxidized hydroethidine staining. Oxidative stress damage in the hippocampal CA1 after ischemia/reperfusion was also significantly attenuated by NSC23766 treatment, as evidenced by a marked attenuation of immunostaining for the oxidative stress damage markers, 4-HNE, 8-OHdG and H2AX at 24 h in the hippocampal CA1 region following cerebral ischemia. In addition, Morris Water maze testing revealed that Rac GTPase inhibition after ischemic injury significantly improved hippocampal-dependent memory and cognitive spatial abilities at 7-9 d post reperfusion as compared to vehicle-treated animals. CONCLUSIONS/SIGNIFICANCE: The results of the study suggest that Rac1 GTPase has a critical role in mediating ischemia/reperfusion injury-induced NADPH oxidase activation, ROS generation and oxidative stress in the hippocampal CA1 region of the rat, and thus contributes significantly to neuronal degeneration and cognitive dysfunction following cerebral ischemia.

Extranuclear estrogen receptors mediate the neuroprotective effects of estrogen in the rat hippocampus.

BACKGROUND: 17beta-estradiol (E2) has been implicated to exert neuroprotective effects in the brain following cerebral ischemia. Classically, E2 is thought to exert its effects via genomic signaling mediated by interaction with nuclear estrogen receptors. However, the role and contribution of extranuclear estrogen receptors (ER) is unclear and was the subject of the current study. METHODOLOGY/PRINCIPAL FINDINGS: To accomplish this goal, we employed two E2 conjugates (E2 dendrimer, EDC, and E2-BSA) that can interact with extranuclear ER and exert rapid nongenomic signaling, but lack the ability to interact with nuclear ER due to their inability to enter the nucleus. EDC or E2-BSA (10 microM) was injected icv 60 min prior to global cerebral ischemia (GCI). FITC-tagged EDC or E2-BSA revealed high uptake in the hippocampal CA1 region after icv injection, with a membrane (extranuclear) localization pattern in cells. Both EDC and E2-BSA exerted robust neuroprotection in the CA1 against GCI, and the effect was blocked by the ER antagonist, ICI182,780. EDC and E2-BSA both rapidly enhanced activation of the prosurvival kinases, ERK and Akt, while attenuating activation of the proapoptotic kinase, JNK following GCI, effects that were blocked by ICI182,780. Administration of an MEK or PI3K inhibitor blocked the neuroprotective effects of EDC and E2-BSA. Further studies showed that EDC increased p-CREB and BDNF in the CA1 region in an ERK- and Akt-dependent manner, and that cognitive outcome after GCI was preserved by EDC in an ER-dependent manner. CONCLUSIONS/SIGNIFICANCE: In conclusion, the current study demonstrates that activation of extranuclear ER results in induction of ERK-Akt-CREB-BDNF signaling in the hippocampal CA1 region, which significantly reduces ischemic neuronal injury and preserves cognitive function following GCI. The study adds to a growing literature that suggests that extranuclear ER can have important actions in the brain.